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1. The Column Space of A Contains All Vectors Ax_2.vtt
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10. Survey of Difficulties with Ax = b.vtt
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11. Minimizing _x_ Subject to Ax = b.vtt
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12. Computing Eigenvalues and Singular Values.vtt
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13. Randomized Matrix Multiplication.vtt
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14. Low Rank Changes in A and Its Inverse.vtt
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15. Matrices A(t) Depending on t, Derivative = dA_dt.vtt
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16. Derivatives of Inverse and Singular Values.vtt
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17. Rapidly Decreasing Singular Values.vtt
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18. Counting Parameters in SVD, LU, QR, Saddle Points.vtt
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19. Saddle Points Continued, Maxmin Principle.vtt
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2. Multiplying and Factoring Matrices.vtt
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20. Definitions and Inequalities.vtt
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21. Minimizing a Function Step by Step.vtt
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22. Gradient Descent- Downhill to a Minimum.vtt
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23. Accelerating Gradient Descent (Use Momentum).vtt
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24. Linear Programming and Two-Person Games.vtt
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25. Stochastic Gradient Descent.vtt
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26. Structure of Neural Nets for Deep Learning.vtt
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27. Backpropagation- Find Partial Derivatives.vtt
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3. Orthonormal Columns in Q Give Q'Q = I.vtt
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30. Completing a Rank-One Matrix, Circulants!.vtt
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31. Eigenvectors of Circulant Matrices- Fourier Matrix.vtt
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32. ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule.vtt
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33. Neural Nets and the Learning Function.vtt
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34. Distance Matrices, Procrustes Problem.vtt
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35. Finding Clusters in Graphs.vtt
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36. Alan Edelman and Julia Language.vtt
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4. Eigenvalues and Eigenvectors.vtt
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5. Positive Definite and Semidefinite Matrices.vtt
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6. Singular Value Decomposition (SVD).vtt
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7. Eckart-Young- The Closest Rank k Matrix to A.vtt
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8. Norms of Vectors and Matrices.vtt
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9. Four Ways to Solve Least Squares Problems.vtt
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An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis, Signal Processing,....vtt
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Course Introduction of 18.065 by Professor Strang.vtt
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